





'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					list[list[int]]					,				__snake_case    :					int					,				__snake_case    :					int					,				__snake_case    :					set     )	->	int:
     __A					,__A		:					str						=			len(__snake_case     ), len(grid[0]     )
     if (
         min(__snake_case					,				__snake_case     ) < 0
         or row == row_length
         or col == col_length
         or (row, col) in visit
         or grid[row][col] == 1
     ):
          return 0
     if row == row_length - 1 and col == col_length - 1:
          return 1

     visit.add((row, col)     )

     __A		:					int						=			0
     count += depth_first_search(__snake_case					,				row + 1					,				__snake_case					,				__snake_case     )
     count += depth_first_search(__snake_case					,				row - 1					,				__snake_case					,				__snake_case     )
     count += depth_first_search(__snake_case					,				__snake_case					,				col + 1					,				__snake_case     )
     count += depth_first_search(__snake_case					,				__snake_case					,				col - 1					,				__snake_case     )

     visit.remove((row, col)     )
     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


